For decades, SAS has been the trusted standard for analyzing and reporting clinical trial data, a tool favored by regulators and embedded in every aspect of submission workflows. Yet, the growing popularity of R has brought new possibilities for advanced modeling, automation, and data visualization.
Organizations now face a complex question: How can R and SAS coexist without disrupting regulatory compliance or operational efficiency? This isn’t a simple “either-or” decision. It’s about building a hybrid model that leverages the strengths of both SAS’s rock-solid compliance and R’s flexibility while avoiding unnecessary disruption to established processes.
Why Clinical Study Reporting Needs Both SAS and R
Clinical Study Reporting (CSR) sits at the heart of regulatory submission. It’s how trial results are analyzed, documented, and communicated to agencies such as the FDA, EMA, and Health Canada. Historically, SAS became dominant due to its reliability, stability, and strong alignment with regulatory expectations.
But as clinical data grows in complexity, teams are turning to R for its:
- Advanced statistical modeling and machine learning capabilities,
- Rich visualization packages (like ggplot2 and Shiny), and
- Cost efficiency through open-source flexibility.
The challenge, however, isn’t just about choosing the best tool. It’s about integrating R into workflows built almost entirely around SAS, without compromising reproducibility, traceability, or compliance. With so much on the line, how does a Biostats team figure out the best path forward?
Two Worlds, Two Starting Points
Not every organization faces this transition in the same way. The journey to a balanced, multi-language analytics environment depends heavily on legacy infrastructure, staff expertise, and regulatory history.
Type 1: Established, SAS-Heavy Organizations
Large pharmaceutical companies and CROs often have decades of SAS assets, global libraries, therapeutic-area templates, macros, and SOPs that align tightly with agency expectations. Their biostats teams are highly proficient in SAS, and every process from dataset creation to output validation revolves around it.
For these organizations, introducing R requires careful consideration. The goal isn’t a complete replacement but a gradual coexistence, allowing R to be used where it adds value (like exploratory modeling or visualization) while keeping SAS as the authoritative system for submission-ready outputs.
Type 2: Newer, More Agile Organizations
Emerging biotech companies or early-stage developers often start with fewer constraints. With limited historical SAS libraries or legacy workflows, they can adopt R more freely. This flexibility allows them to innovate quickly, but it also means they must design new processes that maintain interoperability with regulators and external partners who still depend on SAS.
In both cases, the challenge is balance: leveraging R’s creativity while preserving the trust and compliance foundation that SAS provides.
Practical Paths to SAS and R Integration
Transitioning from a single-language to a multi-language environment isn’t one-size-fits-all. In this article, we outlined several practical approaches, each with its own trade-offs:
- Full replacement of SAS with R – ambitious but risky. It involves rewriting libraries, retraining staff, and validating every process from scratch. Few organizations find this viable as a first step.
- Language coexistence at the study level – retain SAS for global and therapeutic standards, while using R for advanced analytics and visualization within studies.
- Selective use of R for specialized tasks – maintain SAS for core data handling and reporting but allow R to complement it where unique capabilities are needed.
- Full coexistence across all levels – use each language for what it does best: SAS for large-scale data manipulation and R for complex modeling, graphics, and innovation.
Most organizations begin with option 2 or 3, introducing R in low-risk areas and gradually scaling it up as confidence grows.
The Infrastructure Imperative: A Modern SCE
A successful coexistence model depends on a modern Statistical Computing Environment (SCE), one that supports multiple languages seamlessly.
An effective SCE must provide:
- Unified data access and sharing controls
- End-to-end traceability for programs and outputs
- Version control and dependency management
- Automated execution and resource allocation
- Reproducibility and audit readiness across all languages
In short, it must allow SAS, R, Python, and future tools to operate side-by-side without fragmentation or redundant validation cycles. That means consistent user experiences, shared security protocols, and centralized governance.
Without a modern SCE, hybrid programming environments can quickly become inefficient or non-compliant, defeating the purpose of adopting R in the first place. Reach out today if you’d like to modernize your SCE.
Human Factors: Training, Culture, and Collaboration
Technology alone won’t make the transition work. The biggest hurdle often lies in people. Many biostats and programming teams have spent their entire careers mastering SAS. The introduction of R can spark resistance due to its open-source ecosystem, different syntax, and package management. To overcome this, leadership must invest in training, mentorship, and gradual adoption. As with every organization, effective change management can be a game-changer for teams struggling with it.
A practical approach might include:
- Internal workshops and peer learning programs,
- Cross-functional coding sessions to compare R and SAS outputs,
- Encouraging innovation projects that showcase R’s value in visualization or automation.
By emphasizing coexistence rather than competition, teams can view R as an enhancement rather than a replacement.
Managing Compliance, and Reproducibility
Regulators expect consistent, traceable, and reproducible outputs, regardless of programming language. One of the key concerns when adopting R is ensuring that every analysis, dataset, and graphic is fully auditable.
To address this, organizations must:
- Establish transparent governance for code validation and review
- Use controlled package repositories for R
- Document every version of scripts, inputs, and outputs
- Ensure that R-generated results can be validated against SAS where appropriate
The EMA’s Guideline on Computerised Systems and Electronic Data in Clinical Trials (2023) emphasizes exactly these principles: reproducibility, traceability, and system control. Any hybrid approach should align with these expectations to maintain regulatory confidence.
Why Hybrid Makes Sense
R and SAS each bring unique strengths to clinical study reporting. SAS offers reliability, standardized processes, and a proven compliance framework. R delivers flexibility, cutting-edge statistical techniques, and cost savings.
A hybrid model captures both, enabling organizations to:
- Accelerate analysis timelines
- Increase reproducibility through automation
- Expand statistical capabilities without breaking compliance
- Build resilience by future-proofing their analytics infrastructure
The transition doesn’t have to be disruptive. By starting small, selecting the right SCE, and fostering a culture of learning, organizations can create a best-in-class environment that blends the strengths of both tools.
Conclusion
For decades, SAS has powered the life sciences industry’s most critical submissions. R, however, is redefining what’s possible in clinical analytics. The question isn’t which language will win, it’s how organizations can use both to build more innovative, more flexible, and compliant analytics ecosystems.
The future of clinical study reporting is not about replacement but about collaboration, between teams, between systems, and between languages. The companies that succeed will be those that embrace this coexistence with purpose, foresight, and the proper technical foundation. Reach out today if you’re curious how SAS and R can coexist in your organization.
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References
- European Medicines Agency. Guideline on Computerised Systems and Electronic Data in Clinical Trials. EMA, 2023, https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/guideline-computerised-systems-electronic-data-clinical-trials_en.pdf
- U.S. Food and Drug Administration. Computer Software Assurance for Production and Quality System Software: Draft Guidance for Industry and Food and Drug Administration Staff. FDA, 2022, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/computer-software-assurance-production-and-quality-system-software
